This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniq...
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This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.
Our built environment is characterized by large, ever-expanding and highly complex cities. The spatial extent of the interconnected systems that serve these cities leads to higher vulnerability to disruption. On the o...
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Our built environment is characterized by large, ever-expanding and highly complex cities. The spatial extent of the interconnected systems that serve these cities leads to higher vulnerability to disruption. On the other hand, climate change and political instability have noticeably increased the frequency of natural and human-induced hazards. Recalling that risk is the product of vulnerability and hazard, it is evident that large cities are experiencing unprecedented levels of risk. While major investments and numerous research, development and implementation efforts have been dedicated to address natural and human-induced risk to large cities, there is still a lack of system-of-systems level considerations and a comprehensive, interdependent vision for creating cities that respond effectively to severe disruptions. On this note, the authors envision the city of the future, its features and its operational modes. The requirements of creating such smart and sustainable, hence optimally resilient, cities dictate research-to-implementation consequences. A high-level view of these requirements and their implications on research and development is provided.
The revolution in the Internet of Things (IoT) is redesigning and reshaping the healthcare system technologically, economically and socially. The emerging and rapidly growing IoT-based Smart Healthcare System (SHCS) i...
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The revolution in the Internet of Things (IoT) is redesigning and reshaping the healthcare system technologically, economically and socially. The emerging and rapidly growing IoT-based Smart Healthcare System (SHCS) is seen as a sustainable solution to reduce the burden on the existing healthcare system due to increasing diseases and limited medical infrastructure. IoT-based SHCS plays a vital role in delivery of healthcare services in rural and remote areas where the essential medical amenities, necessary infrastructures and qualified medical practitioners are not available. Therefore, in this paper, a comprehensive investigation of futuristic IoT-based SHCS and its constituents is presented. This paper provides exhaustive review on different techniques and technologies dealing with smart healthcare framework, physiological sensing, signal processing, data communication, cloud computing and dataanalytics used in IoT-based SHCS. A comparative analysis of existing literature has been carried out to identify the recent trends and advancements in this very dynamic field of global importance. In addition to this, it highlights different issues and challenges, along with the recommendation for further research in the field. The prime objective of this paper is to deliver the state-of-the-art understanding and update about IoT-based SHCS and its constituents by providing a good source of information to the researchers, service providers, technologists, medical practitioners and the general population.
The Permanent Downhole Gauge (PDG) pressure measurement is of great importance for offshore oil well modeling and control since it is measured close to the bottom hole. The PDG is installed in a remote undersea enviro...
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The Permanent Downhole Gauge (PDG) pressure measurement is of great importance for offshore oil well modeling and control since it is measured close to the bottom hole. The PDG is installed in a remote undersea environment, which makes expensive the maintenance in case of fault. For this reason, PDG measurements are frequently unavailable. To overcome this limitation, the PDG pressure can be estimated using other available measurements. The estimation is not a simple task since, depending on process operational conditions, the multiphase flow might present limit cycles. In this work, Artificial Neural Network (ANN) and Extended Kalman Filter (EKF) are proposed as potential techniques for the PDG pressure estimation. The comparison of the results shows that ANN returns precise estimation for a short-time window after the failure, but fails when a different process operating condition is applied, while EKF returns good estimation in all the cases. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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